Multi-Objective Algorithm for Blood Supply via Unmanned Aerial Vehicles to the Wounded in an Emergency Situation.

Wen T, Zhang Z, Wong KK - PLoS ONE (2016)

Bottom Line:
This is a complex problem that includes maintenance of the supply blood's temperature model during transportation, the UAVs' scheduling and routes' planning in case of multiple sites requesting blood, and limited carrying capacity.Then, by introducing the idea of transportation appendage into the traditional Capacitated Vehicle Routing Problem (CVRP), this new problem is proposed according to the factors of distance and weight.By comparing our technique with the traditional ones, our algorithm can obtain better optimization results and time performance.

ABSTRACTUnmanned aerial vehicle (UAV) has been widely used in many industries. In the medical environment, especially in some emergency situations, UAVs play an important role such as the supply of medicines and blood with speed and efficiency. In this paper, we study the problem of multi-objective blood supply by UAVs in such emergency situations. This is a complex problem that includes maintenance of the supply blood's temperature model during transportation, the UAVs' scheduling and routes' planning in case of multiple sites requesting blood, and limited carrying capacity. Most importantly, we need to study the blood's temperature change due to the external environment, the heating agent (or refrigerant) and time factor during transportation, and propose an optimal method for calculating the mixing proportion of blood and appendage in different circumstances and delivery conditions. Then, by introducing the idea of transportation appendage into the traditional Capacitated Vehicle Routing Problem (CVRP), this new problem is proposed according to the factors of distance and weight. Algorithmically, we use the combination of decomposition-based multi-objective evolutionary algorithm and local search method to perform a series of experiments on the CVRP public dataset. By comparing our technique with the traditional ones, our algorithm can obtain better optimization results and time performance.

pone.0155176.g009: Temperature changes with time in the case of different weight of hot water and blood.Note: Red line represents the blood temperature is beyond the appropriate range.

Mentions:
It can be seen from Fig 8 that the temperature of the water is decreasing with time whereas the temperature of the blood is increasing during the time as the water temperature is decreasing. In the problem, we must ensure that the temperature of the blood is in an appropriate range during the transit. If the weight of the hot water is relatively heavier than the weight of the blood, the blood temperature will rise rapidly in a short period of time. In Fig 9(A), when the ratio of the weight of hot water to the weight of blood is 4.4, the blood temperature can be kept between 2–10°C (in the appropriate range) within 4.5 hours, and above 10°C between 4.5 hours and 12.6 hours. Then in Fig 9(B), we increase the initial temperature of hot water, the blood temperature will be above 10°C within just 2 hours and last for more than 20 hours. Therefore, during the transit, the change of the blood temperature depends on the initial temperature and the weight of hot water, and it’s not practical to increase the initial temperature of the hot water for the goal of reducing its total weight.

pone.0155176.g009: Temperature changes with time in the case of different weight of hot water and blood.Note: Red line represents the blood temperature is beyond the appropriate range.

Mentions:
It can be seen from Fig 8 that the temperature of the water is decreasing with time whereas the temperature of the blood is increasing during the time as the water temperature is decreasing. In the problem, we must ensure that the temperature of the blood is in an appropriate range during the transit. If the weight of the hot water is relatively heavier than the weight of the blood, the blood temperature will rise rapidly in a short period of time. In Fig 9(A), when the ratio of the weight of hot water to the weight of blood is 4.4, the blood temperature can be kept between 2–10°C (in the appropriate range) within 4.5 hours, and above 10°C between 4.5 hours and 12.6 hours. Then in Fig 9(B), we increase the initial temperature of hot water, the blood temperature will be above 10°C within just 2 hours and last for more than 20 hours. Therefore, during the transit, the change of the blood temperature depends on the initial temperature and the weight of hot water, and it’s not practical to increase the initial temperature of the hot water for the goal of reducing its total weight.

Bottom Line:
This is a complex problem that includes maintenance of the supply blood's temperature model during transportation, the UAVs' scheduling and routes' planning in case of multiple sites requesting blood, and limited carrying capacity.Then, by introducing the idea of transportation appendage into the traditional Capacitated Vehicle Routing Problem (CVRP), this new problem is proposed according to the factors of distance and weight.By comparing our technique with the traditional ones, our algorithm can obtain better optimization results and time performance.

ABSTRACTUnmanned aerial vehicle (UAV) has been widely used in many industries. In the medical environment, especially in some emergency situations, UAVs play an important role such as the supply of medicines and blood with speed and efficiency. In this paper, we study the problem of multi-objective blood supply by UAVs in such emergency situations. This is a complex problem that includes maintenance of the supply blood's temperature model during transportation, the UAVs' scheduling and routes' planning in case of multiple sites requesting blood, and limited carrying capacity. Most importantly, we need to study the blood's temperature change due to the external environment, the heating agent (or refrigerant) and time factor during transportation, and propose an optimal method for calculating the mixing proportion of blood and appendage in different circumstances and delivery conditions. Then, by introducing the idea of transportation appendage into the traditional Capacitated Vehicle Routing Problem (CVRP), this new problem is proposed according to the factors of distance and weight. Algorithmically, we use the combination of decomposition-based multi-objective evolutionary algorithm and local search method to perform a series of experiments on the CVRP public dataset. By comparing our technique with the traditional ones, our algorithm can obtain better optimization results and time performance.